{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mlsp_5assignment_q2",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "oFH7NxbcuBaW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "534c6855-44c4-4a19-e4c0-8aca593650ed"
      },
      "cell_type": "code",
      "source": [
        "!pip install mnist"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mnist in /usr/local/lib/python3.6/dist-packages (0.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mnist) (1.14.6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cc8GlL_gwEXZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PNtPMvQJvWI7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "6cfc4128-0c1d-4261-b073-30821af7e3ba"
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.examples.tutorials import mnist\n",
        "\n",
        "mnist_data = mnist.input_data.read_data_sets('/tmp')\n",
        "train_data = mnist_data.train\n",
        "test_data = mnist_data.test"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting /tmp/train-images-idx3-ubyte.gz\n",
            "Extracting /tmp/train-labels-idx1-ubyte.gz\n",
            "Extracting /tmp/t10k-images-idx3-ubyte.gz\n",
            "Extracting /tmp/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "enlz6qc2wWwh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42799d92-49d6-468a-edfa-473799986dcb"
      },
      "cell_type": "code",
      "source": [
        "train_data.images.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "Clz2te1bwhF4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "44d3dfb7-db5a-4d52-bc4a-9f3a23d72a64"
      },
      "cell_type": "code",
      "source": [
        "test_data.images.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "OOSr0SIPvjga",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "72babba1-28f4-4fca-8ced-f5a016bbf689"
      },
      "cell_type": "code",
      "source": [
        "type(train_data.labels)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "euou_J2PtpGY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "27dc0021-89a5-4866-f8d4-5a9cfb66f751"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# images = mnist.train_images()\n",
        "# labels = mnist.train_labels()\n",
        "\n",
        "images = train_data.images\n",
        "labels = train_data.labels\n",
        "test_images = test_data.images\n",
        "test_labeks = test_data.labels\n",
        "\n",
        "# test_images = mnist.test_images()\n",
        "# test_labels = mnist.test_labels()\n",
        "images = images.reshape(55000,28,28,1)\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
        "input_shape=(28,28,1)\n",
        "num_classes = 10\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Conv2D(128, kernel_size=(3, 3),\n",
        "                 activation='relu',\n",
        "                 input_shape=input_shape))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.Adadelta(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.001), \n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "cnn_train = model.fit(images, labels, epochs=20,validation_split=0.10,batch_size = 200)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 49500 samples, validate on 5500 samples\n",
            "Epoch 1/20\n",
            "49500/49500 [==============================] - 13s 264us/step - loss: 0.2022 - acc: 0.9374 - val_loss: 0.0603 - val_acc: 0.9827\n",
            "Epoch 2/20\n",
            "49500/49500 [==============================] - 12s 242us/step - loss: 0.0516 - acc: 0.9837 - val_loss: 0.0517 - val_acc: 0.9862\n",
            "Epoch 3/20\n",
            "49500/49500 [==============================] - 12s 242us/step - loss: 0.0292 - acc: 0.9907 - val_loss: 0.0535 - val_acc: 0.9864\n",
            "Epoch 4/20\n",
            "49500/49500 [==============================] - 12s 242us/step - loss: 0.0199 - acc: 0.9935 - val_loss: 0.0528 - val_acc: 0.9876\n",
            "Epoch 5/20\n",
            "49500/49500 [==============================] - 12s 241us/step - loss: 0.0134 - acc: 0.9956 - val_loss: 0.0592 - val_acc: 0.9856\n",
            "Epoch 6/20\n",
            "49500/49500 [==============================] - 12s 240us/step - loss: 0.0115 - acc: 0.9963 - val_loss: 0.0605 - val_acc: 0.9878\n",
            "Epoch 7/20\n",
            "49500/49500 [==============================] - 12s 240us/step - loss: 0.0080 - acc: 0.9973 - val_loss: 0.0673 - val_acc: 0.9864\n",
            "Epoch 8/20\n",
            "49500/49500 [==============================] - 12s 240us/step - loss: 0.0111 - acc: 0.9963 - val_loss: 0.0537 - val_acc: 0.9875\n",
            "Epoch 9/20\n",
            "49500/49500 [==============================] - 12s 239us/step - loss: 0.0085 - acc: 0.9973 - val_loss: 0.0582 - val_acc: 0.9865\n",
            "Epoch 10/20\n",
            "49500/49500 [==============================] - 12s 239us/step - loss: 0.0071 - acc: 0.9973 - val_loss: 0.0603 - val_acc: 0.9865\n",
            "Epoch 11/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0067 - acc: 0.9980 - val_loss: 0.0594 - val_acc: 0.9873\n",
            "Epoch 12/20\n",
            "49500/49500 [==============================] - 12s 239us/step - loss: 0.0060 - acc: 0.9978 - val_loss: 0.0710 - val_acc: 0.9865\n",
            "Epoch 13/20\n",
            "49500/49500 [==============================] - 12s 239us/step - loss: 0.0064 - acc: 0.9981 - val_loss: 0.0705 - val_acc: 0.9869\n",
            "Epoch 14/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0030 - acc: 0.9989 - val_loss: 0.0665 - val_acc: 0.9882\n",
            "Epoch 15/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0040 - acc: 0.9987 - val_loss: 0.0814 - val_acc: 0.9862\n",
            "Epoch 16/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0071 - acc: 0.9974 - val_loss: 0.0644 - val_acc: 0.9885\n",
            "Epoch 17/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0041 - acc: 0.9989 - val_loss: 0.0671 - val_acc: 0.9893\n",
            "Epoch 18/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0046 - acc: 0.9984 - val_loss: 0.0707 - val_acc: 0.9885\n",
            "Epoch 19/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0055 - acc: 0.9982 - val_loss: 0.0604 - val_acc: 0.9887\n",
            "Epoch 20/20\n",
            "49500/49500 [==============================] - 12s 238us/step - loss: 0.0034 - acc: 0.9989 - val_loss: 0.0744 - val_acc: 0.9862\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K6y45afPuppj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "af06cdc1-227a-4c0f-c4b8-8a07f8771ab5"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(cnn_train.history['loss'],label =\"train loss\")\n",
        "plt.plot(cnn_train.history['val_loss'],label =\" val loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LT1sIIYQ/6PWhbTJoqaptpL7B6e2mCCGEEO3q9aHtfuyrSh77EkII4dt6fWibjHIzmhBC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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff6c87f1128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Crnj0T0qzfTD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4a87790c-40d2-4ce5-fc78-7995dcb37817"
      },
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(test_images.reshape(10000,28,28,1),test_labeks)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 153us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TLOt9twhz0I5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "515adaf1-688e-4531-f461-1090e2641c16"
      },
      "cell_type": "code",
      "source": [
        "print(acc)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9817\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wC0miSkhz2Hi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wD4JxUHkz76L",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Using maxpool and 2 filters"
      ]
    },
    {
      "metadata": {
        "id": "HYs4tLLc0BCy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1278
        },
        "outputId": "7876bc2a-e509-407d-f8ea-067737bab808"
      },
      "cell_type": "code",
      "source": [
        "input_shape=(28,28,1)\n",
        "num_classes = 10\n",
        "model2 = keras.Sequential()\n",
        "model2.add(keras.layers.Conv2D(128, kernel_size=(3, 3),\n",
        "                 activation='relu',\n",
        "                 input_shape=input_shape))\n",
        "model2.add(keras.layers.Conv2D(64, (5, 5), activation='relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model2.add(Dropout(0.25))\n",
        "model2.add(Flatten())\n",
        "model2.add(Dense(128, activation='relu'))\n",
        "model2.add(Dropout(0.2))\n",
        "model2.add(Dense(256, activation='relu'))\n",
        "model2.add(Dropout(0.2))\n",
        "model2.add(Dense(512, activation='relu'))\n",
        "model2.add(Dropout(0.2))\n",
        "model2.add(Dense(num_classes, activation='softmax'))\n",
        "model2.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
        "              optimizer=keras.optimizers.Adadelta(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "print(model2.summary())\n",
        "cnn_maxpool_train = model2.fit(images, labels, epochs=20,validation_split=0.10)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 26, 26, 128)       1280      \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 22, 22, 64)        204864    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 11, 11, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 7744)              0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 128)               991360    \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 256)               33024     \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 512)               131584    \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,367,242\n",
            "Trainable params: 1,367,242\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Train on 49500 samples, validate on 5500 samples\n",
            "Epoch 1/20\n",
            "49500/49500 [==============================] - 24s 476us/step - loss: 0.1913 - acc: 0.9405 - val_loss: 0.0556 - val_acc: 0.9845\n",
            "Epoch 2/20\n",
            "49500/49500 [==============================] - 23s 456us/step - loss: 0.0635 - acc: 0.9822 - val_loss: 0.0351 - val_acc: 0.9898\n",
            "Epoch 3/20\n",
            "49500/49500 [==============================] - 23s 456us/step - loss: 0.0465 - acc: 0.9868 - val_loss: 0.0415 - val_acc: 0.9898\n",
            "Epoch 4/20\n",
            "49500/49500 [==============================] - 23s 455us/step - loss: 0.0357 - acc: 0.9896 - val_loss: 0.0320 - val_acc: 0.9913\n",
            "Epoch 5/20\n",
            "49500/49500 [==============================] - 23s 455us/step - loss: 0.0279 - acc: 0.9919 - val_loss: 0.0320 - val_acc: 0.9924\n",
            "Epoch 6/20\n",
            "49500/49500 [==============================] - 23s 455us/step - loss: 0.0262 - acc: 0.9922 - val_loss: 0.0292 - val_acc: 0.9922\n",
            "Epoch 7/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0235 - acc: 0.9933 - val_loss: 0.0391 - val_acc: 0.9916\n",
            "Epoch 8/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0197 - acc: 0.9943 - val_loss: 0.0358 - val_acc: 0.9915\n",
            "Epoch 9/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0171 - acc: 0.9951 - val_loss: 0.0302 - val_acc: 0.9927\n",
            "Epoch 10/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0143 - acc: 0.9956 - val_loss: 0.0560 - val_acc: 0.9905\n",
            "Epoch 11/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0163 - acc: 0.9958 - val_loss: 0.0395 - val_acc: 0.9935\n",
            "Epoch 12/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0151 - acc: 0.9957 - val_loss: 0.0359 - val_acc: 0.9924\n",
            "Epoch 13/20\n",
            "49500/49500 [==============================] - 23s 455us/step - loss: 0.0125 - acc: 0.9964 - val_loss: 0.0434 - val_acc: 0.9918\n",
            "Epoch 14/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0104 - acc: 0.9972 - val_loss: 0.0403 - val_acc: 0.9915\n",
            "Epoch 15/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0096 - acc: 0.9974 - val_loss: 0.0406 - val_acc: 0.9935\n",
            "Epoch 16/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0096 - acc: 0.9974 - val_loss: 0.0427 - val_acc: 0.9931\n",
            "Epoch 17/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0093 - acc: 0.9974 - val_loss: 0.0462 - val_acc: 0.9916\n",
            "Epoch 18/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0081 - acc: 0.9978 - val_loss: 0.0536 - val_acc: 0.9927\n",
            "Epoch 19/20\n",
            "49500/49500 [==============================] - 22s 453us/step - loss: 0.0082 - acc: 0.9979 - val_loss: 0.0498 - val_acc: 0.9920\n",
            "Epoch 20/20\n",
            "49500/49500 [==============================] - 22s 454us/step - loss: 0.0083 - acc: 0.9977 - val_loss: 0.0525 - val_acc: 0.9940\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8MAFIl0u1w5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "19337667-2138-4c1e-d9da-5e500aeabee9"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(cnn_maxpool_train.history['loss'],label =\"train loss\")\n",
        "plt.plot(cnn_maxpool_train.history['val_loss'],label =\" val loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vvPACQUFBQ1BW/3jv9lXXTLyuT/8lQgghxLDrtae9a9cuCgoKyMrK4tlnn+XZZ5/tsnzN\nmjVMmTLlnPUuv/xy/vKXv/CXv/yFp556CoB169Zxxx138Prrr5OUlMT69et9VMbgdNxX2yHHtYUQ\nQvixXkM7Ozub+fPnA5CamkpNTQ1Op9O7/NFHH/Uu783OnTuZN28eAHPmzCE7O3sgbfY5ua+2EEKI\n0aDX0Lbb7ZhMJu9js9mMzWbzPjYYDN2ud/z4ce677z5uv/12vvjiCwAaGxu9w+GRkZFdtjOSzHJf\nbSGEEKNAvw/gejyeXl+TnJzMAw88wHXXXUdRURHLli1j8+bN/d6OyaQnIEDb3yb2ymIxdnk8AQWA\nxlb3OctGi9Ha7t6osS6pafRQY11qrAnUW9fZeg1tq9WK3W73Pq6oqMBisfS4TnR0NIsWLQIgMTGR\nqKgoysvL0ev1NDU1ERwcTHl5OVartcftOBwNfamhXywWIzZbXZfnPKdvy1laUXfOstGgu5rUQI11\nSU2jhxrrUmNNoL66evoC0uvweGZmJps2bQIgJycHq9V63iHxDhs3buTVV18FwGazUVlZSXR0NDNn\nzvRua/PmzVx11VV9LmIo6QK1hAYHUCVTmQohhPBjvfa0p0+fTnp6OkuXLkVRFFatWsWGDRswGo0s\nWLCAhx56iLKyMvLz87n77rtZsmQJc+fO5fHHH+ejjz6itbWVp59+mqCgIB588EGeeOIJsrKyiIuL\n4+abbx6OGvvEHBZMhaMRj8eDoigj3RwhhBDiHH06pv344493eTx58mTvv9etW9ftOi+99NI5z1mt\nVl577bX+tG/YmI06iiqcNDa3oQ8OHOnmCCGEEOeQGdFOk/tqCyGE8HcS2qd5L/uSCVaEEEL4KQnt\n00xyi04hhBB+TkL7tDPzj0tPWwghhH+S0D7NO/+49LSFEEL4KQnt00yd7vQlhBBC+CMJ7dMCAzSE\n6QNl/nEhhBB+S0K7E1NYMFV1zX2aF10IIYQYbhLanZiNOlrb3DgbW0e6KUIIIcQ5JLQ7kftqCyGE\n8GcS2p14zyCXk9GEEEL4IQntTkwyK5oQQgg/JqHdiQyPCyGE8GcS2p3I/ONCCCH8mYR2JxEGHQrS\n0xZCCOGfJLQ7CdBqCDMEyQQrQggh/JKE9lnMxmCqnc24ZYIVIYQQfkZC+yzmMB1tLg91DTLBihBC\nCP8ioX2WM2eQyxC5EEII/yKhfRbvGeRyMpoQQgg/I6F9FpNRLvsSQgjhnyS0z2IOax8ed0hPWwgh\nhJ+R0D6LWXraQggh/JSE9lkiDDo0ikKV3DRECCGEn5HQPotGoxBhDMIhZ48LIYTwMxLa3TAbg3HU\nteB2ywQrQggh/IeEdjdMRh1uj4ea+paRbooQQgjhJaHdjTPXassQuRBCCP8R0JcXrV69mv3796Mo\nCitWrCAjI8O7rLm5mZUrV5Kbm8uGDRu8z69Zs4a9e/fS1tbG97//fRYuXMiTTz5JTk4OERERANx7\n771cffXVvq3IBzpmRXPIyWhCCCH8SK+hvWvXLgoKCsjKyiIvL48VK1aQlZXlXb5mzRqmTJlCbm6u\n97kdO3aQm5tLVlYWDoeDW265hYULFwLwwx/+kDlz5gxBKb4jPW0hhBD+qNfQzs7OZv78+QCkpqZS\nU1OD0+nEYDAA8Oijj1JdXc3GjRu961x22WXe3nhYWBiNjY24XK6haP+Q6JhgRS77EkII4U96PaZt\nt9sxmUzex2azGZvN5n3cEd6dabVa9Ho9AOvXr2fWrFlotVoA/vrXv7Js2TIeffRRqqqqBl3AUPBO\nsCI9bSGEEH6kT8e0O/P04z7TW7ZsYf369fzhD38A4Jvf/CYRERFMmTKFl19+mV/96lesXLnyvOub\nTHoCArT9bWKvLBZjj8sjIw0EaBXqGtt6fa2/GC3t7C811iU1jR5qrEuNNYF66zpbr6FttVqx2+3e\nxxUVFVgsll43vG3bNl566SVeeeUVjMb2/8wZM2Z4l8+dO5enn366x204HA29vk9/WSxGbLa6Xl8X\nYdBRXlXfp9eOtL7WNNqosS6pafRQY11qrAnUV1dPX0B6HR7PzMxk06ZNAOTk5GC1WrsdEu+srq6O\nNWvW8Lvf/c57pjjAgw8+SFFREQA7d+4kLS2tTwWMBLNRR42zhTaXe6SbIoQQQgB96GlPnz6d9PR0\nli5diqIorFq1ig0bNmA0GlmwYAEPPfQQZWVl5Ofnc/fdd7NkyRIaGhpwOBw88sgj3u08//zz3Hnn\nnTzyyCOEhISg1+t57rnnhrS4wTCHBeOhhhpnC5HhwSPdHCGEEKJvx7Qff/zxLo8nT57s/fe6deu6\nXee2224757m4uDjeeuut/rRvxJjCztztS0JbCCGEP5AZ0c6jY4KVKrmvthBCCD8hoX0ecl9tIYQQ\n/kZC+zy8E6xIT1sIIYSfkNA+j45j2jL/uBBCCH8hoX0expBAAgM0MiuaEEIIvyGhfR6KomAy6mT+\ncSGEEH5DQrsHZqOO2voWWttkghUhhBAjT0K7B6aO+2o7pbcthBBi5Elo96DjvtoOOa4thBDCD0ho\n90Duqy2EEMKfSGj3QO6rLYQQwp9IaPdAetpCCCH8iYR2D84c05bQFkIIMfIktHug1wUQFCgTrAgh\nhPAPEto9UBQFszFYhseFEEL4BQntXpjDdDgbW2lpdY10U4QQQoxxEtq96Livttw4RAghxEiT0O5F\nx8loclxbCCHESJPQ7oVc9iWEEMJfSGj3wiQTrAghhPATEtq98M6KJj1tIYQQI0xCuxcdw+NyIpoQ\nQoiRJqHdixBdACE6rQyPCyGEGHES2n1gNgZTJVOZCiGEGGES2n1gCtPR0NxGU0vbSDdFCCHEGCah\n3QdnbtEpvW0hhBAjR0K7DzpmRauqk+PaQgghRo6Edh+Y5BadQggh/ICEdh/IrGhCCCH8QUBfXrR6\n9Wr279+PoiisWLGCjIwM77Lm5mZWrlxJbm4uGzZs6HGdU6dOsXz5clwuFxaLhRdeeIGgoCDfV+Vj\nZpkVTQghhB/otae9a9cuCgoKyMrK4tlnn+XZZ5/tsnzNmjVMmTKlT+usW7eOO+64g9dff52kpCTW\nr1/vw1KGzplj2tLTFkIIMXJ6De3s7Gzmz58PQGpqKjU1NTidTu/yRx991Lu8t3V27tzJvHnzAJgz\nZw7Z2dk+K2Qo6YK0hAYHSE9bCCHEiOp1eNxut5Oenu59bDabsdlsGAwGAAwGA9XV1X1ap7Gx0Tsc\nHhkZic1m6/G9TSY9AQHavlfTRxaLsf/rmPSUV9UTFWVAURSft2mwBlLTaKDGuqSm0UONdamxJlBv\nXWfr0zHtzjweT7/fpLt1+rIdh6Oh3+/VG4vFiM1W1+/1wvSBnDzlorC4Gn1wv//bhtRAa/J3aqxL\naho91FiXGmsC9dXV0xeQXofHrVYrdrvd+7iiogKLxTKgdfR6PU1N7UPM5eXlWK3WXhvvL86cQS5D\n5EIIIUZGr6GdmZnJpk2bAMjJycFqtXqHxvu7zsyZM73Pb968mauuumqw7R82MiuaEEKIkdbrOO/0\n6dNJT09n6dKlKIrCqlWr2LBhA0ajkQULFvDQQw9RVlZGfn4+d999N0uWLOHGG288Zx2ABx98kCee\neIKsrCzi4uK4+eabh7xAXzGHddxXW3raQgghRkafDs4+/vjjXR5PnjzZ++9169b1aR1oHzZ/7bXX\n+tM+v2HquOxLetpCCCFGiMyI1kdm71Sm0tMWQggxMiS0+8h7TFsmWBFCCDFCJLT7KDBAi1EfKKEt\nhBBixEho94PZGIyjtmlA16oLIYQQgyWh3Q/mMB0tbW7qm9pGuilCCCHGIAntfjDJ3b6EEEKMIAnt\nfpD7agshhBhJEtr90HEGuVz2JYQQYiRIaPeD9LSFEEKMJAntfjDLMW0hhBAjSEK7HyKMOhRkKlMh\nhBAjQ0K7HwK0GsJCg+SmIUIIIUaEhHY/mcN0OOqaZYIVIYQQw05Cu5/MxmDaXB7qGlpHuilCCCHG\nGAntfjLJfbWFEEKMEAntfjLLfbWFEEKMEAntfuq4r7Zc9iWEEGK4SWj3k7enLROsCCGEGGYS2v3U\n0dN2SGgLIYQYZhLa/RRuCEJRZHhcCCHE8JPQ7ietRkOEQScnogkhhBh2EtoDYA7TUe1sxu2WCVaE\nEEIMHwntATAbg3G5PdTUt4x0U4QQQowhEtoDYDLKBCtCCCGGn4T2AHTcV9shx7WFEEIMIwntAfDe\nV1su+xJCCDGMJLQHoKOnLZd9CSGEGE4S2gPgncpUetpCCCGGUUBfXrR69Wr279+PoiisWLGCjIwM\n77Lt27fz4osvotVqmTVrFvfffz9vvvkmGzdu9L7m4MGD7Nu3j7vvvpuGhgb0ej0ATzzxBNOmTfNx\nSUMvLDQIrUbBIT1tIYQQw6jX0N61axcFBQVkZWWRl5fHihUryMrK8i5/5plnePXVV4mOjuauu+7i\nmmuuYfHixSxevNi7/vvvv+99/XPPPcfEiROHoJTho1GU9glWpKcthBBiGPU6PJ6dnc38+fMBSE1N\npaamBqfTCUBRURHh4eHExsai0WiYPXs22dnZXdb/9a9/zQ9+8IMhaPrI6phgxeV2j3RThBBCjBG9\n9rTtdjvp6enex2azGZvNhsFgwGazYTabuywrKiryPj5w4ACxsbFYLBbvc+vWrcPhcJCamsqKFSsI\nDg4+73ubTHoCArT9Lqo3Fotx0NuItRjILa5BGxSExRTig1YNji9q8kdqrEtqGj3UWJcaawL11nW2\nPh3T7szj6fvUnevXr+eWW27xPl62bBmTJk0iMTGRVatW8be//Y177733vOs7HA39bV6vLBYjNlvd\noLcTGtT+ZeL4yUpoCx/09gbDVzX5GzXWJTWNHmqsS401gfrq6ukLSK/D41arFbvd7n1cUVHh7Tmf\nvay8vByr1ep9vHPnTi6++GLv4wULFpCYmAjA3LlzOXbsWD/K8C/ey75kVjQhhBDDpNfQzszMZNOm\nTQDk5ORgtVoxGAwAJCQk4HQ6KS4upq2tja1bt5KZmQm0B3hoaChBQUFAew/9nnvuoba2FmgP9LS0\ntCEpajh4J1iRWdGEEEIMk16Hx6dPn056ejpLly5FURRWrVrFhg0bMBqNLFiwgKeffprHHnsMgEWL\nFjF+/HiAc453K4rCkiVLuOeeewgJCSE6OpoHH3xwiMoaeqaOa7Xlsi8hhBDDRPH05yD1MBuKYxS+\nOvZRW9/CI7/8nEsmWrj/1gt80LKBU9vxnA5qrEtqGj3UWJcaawL11TWoY9qie0Z9IAFajRzTFkII\nMWwktAdIURTMRp0c0xZCCDFsJLQHwRymo7a+hTaXTLAihBBi6EloD4LJGIwHcMh0pkIIIYaBhPYg\nmOUMciGEEMNIQnsQOq7Vlp62EEKI4SChPQgm76xoEtpCCCGGnoT2IJyZFU2Gx4UQQgw9Ce1B8M4/\nLpd9CSGEGAYS2oMQGhxAUKBMsCKEEGJ4SGgPgqIomIzBVNY00dom12oLIYQYWhLagzQ12UR9Uxt/\n+uBIv+41LoQQQvSXhPYg3TZnAilxYWw/WMa72QUj3RwhhBAqJqE9SEGBWh78VgaRYcG8/dkJdh0u\nH+kmCSGEUCkJbR8IDw3i4cUZBAdpeeXdw+SV1Ix0k4QQQqiQhLaPJFgM/NfN03C53fzyrQPYqxtH\nuklCCCFURkLbhy5IieTOBROpbWjlF+sP0NDUNtJNEkIIoSIS2j42d3oC8y9NoMRez2//eRCXWy4F\nE0II4RsS2kNg6dw0MlIjycmv4vUPc+VSMCGEED4hoT0ENBqF79+UToLFwNZ9JWzZUzzSTRJCCKEC\nEtpDJEQXwCOLMwgPDeKNj3L5Ktc+0k0SQggxyo2p0G5qaxrWoWpzWDAPfTuDwAANv9uYQ2F53bC9\ntxCdeTwe1uduZMPxd3F75DwLIUarMRPaTW3N/OiLZ/jNrj8P6/uOjw3jezdOpbnVxS/WH8Ah994W\nI+Bg5WG2Fn3OR4WfsTHvg5FujhBigMZMaOu0QcSHxvDpyR3sLts3rO99ySQri69OxVHXzLq3DtDc\n4hrW9xdjm8vt4u3j76GgEBls4sPCT9hWkj3SzRJCDMCYCW1FUVg2dSm6AB1Zx96mqskxrO9/7RWJ\nXJURS0FZHS//Kwe3nFEuhskXpbsob6hgZtzlPHTxf2IIDCXr6DsctB8e6aYJ0a2qJgc1zbUj3Qy/\npH366aefHulGnE9DQ4tPtxc1o9mgAAAgAElEQVQaqCfOHMWO4i8privl8pjpKIri0/c4H0VRuCAl\nktziag7mV9Ha5iZ9vNkn2w4N1fn8/8ofqLGu4a6psa2J33/9ZxRF4T8zvoMpOIIJESnsLt/HPtvX\nTI2cSLgubFDvocb9BOqsy99rKqsv541j7/D6kbf4qOgz9lUcoKLBjsvjIjzISIAmoNv1/L2u/goN\n1Z132ZgKbYD0+FRyy09yqOoYwQE6UsKTff4e56PRKFyUFsW+Y3a+Om7HZNSRFGMc9HbV9gPbQY11\nDXdN7+V/yOGqYywaP5/0yMkAmILDiQmNZk/ZPr62H+JiawYhASEDfg817idQZ13+WlNFg503j23k\njaNvc6q+nERjPHGGWIqdp8irOcme8q/YUvgph6uO4WhyoFE0hAeFoVHaB4uHuy6X20VZfQVHqnLJ\nqTxCbGg0gdpAn22/p9Du/muLiimKwu2Tv8WJXQVszPuAyaY0Eoxxw/b+ocGBPLw4g2f/vJe/bDpK\nVHgwU5N90+MWorPKRgcfF20jQhfO3HFXdVl2kWUat6bdwFu5/+K3+1/jh5f816CCW4iBqGx08MHJ\nLewo24vb4ybeEMv14xeSETUVRVFodbeRX1PA0apcjjiOk19TwImak7x3cgs6bRBpESlMMqcxI/BC\ngj3GIRk5bWxrpMRZRrGzlJK6UoqdpzhVX0ar+8w01ZaQKC62XuDz9+6O4unDNVCrV69m//79KIrC\nihUryMjI8C7bvn07L774IlqtllmzZnH//fezc+dOHn74YdLS0gCYOHEiTz31FKdOnWL58uW4XC4s\nFgsvvPACQUFB531fm833l0hZLEZstjpyKo/wm/1/IDY0muWXPkSQD78l9cWxomrWvrGPwAAtP152\nCbGRoQPeVkdNaqPGuoazptdyXmdP+Vcsm3IbV8Re0u1r3jz2Tz4p/oJJpgn84MLvnnf4sSdq3E+g\nzrr8pabq5ho+OPkx20t34fK4iNZbuX78Ai62XuDtPXenobWR3Oo8jlQd56gjl/IGm3dZWJCRSaY0\nJpsnMMk0AVNwRL/a5PF4qGqqbg9nZ3s4l9SVYm+q6vK6AEVLbGg08cY4EgxxJBoTSAlP8ukXBovl\n/COwvX5Cd+3aRUFBAVlZWeTl5bFixQqysrK8y5955hleffVVoqOjueuuu7jmmmsAuPzyy1m3bl2X\nba1bt4477riD6667jhdffJH169dzxx13DLSuQUmPnMys+Jl8VrKdjXnv8+2JNw3r+08cF8F/XDeF\n3797iP97cz8/XnYpRv35v8AI0R8nawvZU/4V44zxXBZz8Xlf9620G6lqquaAPYe/H9nAXVMWD9t5\nHmLsqW2pY3PBVraV7KDN3UZUSCTXj1/ApdEX9RjWHfSBIVxomcaFlmkAOJqqOeI4zsmGk+w/dYjd\n5V+yu/xLAKL11tMBnsZEU0qXkaRWdxtl9eUU15VS4jxF8emQbmzrendGQ2Aok0wTSDDEkWCMI94Q\nS4zeilaj9eH/Sv/0GtrZ2dnMnz8fgNTUVGpqanA6nRgMBoqKiggPDyc2NhaA2bNnk52dzcSJE7vd\n1s6dO/nJT34CwJw5c/jDH/4wYqENcMuERRx1HGdr8eekR01mirn7dg+VGdNiKKtq4F/bT/LLDV/z\n30svJjBgzJzQL4aIx+NhQ+6/Abh1wg09/jLUKBr+I/12/u/L37GjbA+RISYWjV8wXE0VY4SztZ4t\nBZ/yafEXtLhbMekiWDR+PlfEXDKoADQFRzAj9lJussyhoqKWU/XlHHHkcrQql2PVJ/i0eDufFm9H\nQSE5bByRIWZKnWWUNVR0mWRIQcGij2SKOY14QxwJhlgSjHGEB4X53ZfYXkPbbreTnp7ufWw2m7HZ\nbBgMBmw2G2azucuyoqIiJk6cyPHjx7nvvvuoqanhgQceIDMzk8bGRu9weGRkJDab7Zz368xk0hMQ\n4PtvNJ2HHh7N/C7/s2UNfzv6Jmuv+TFGncHn79eT792aQU1DK599VcLftx7nh7cP7Iz2noZTRjM1\n1jXUNe0q/oq8mnwujcsgc+JFfVrnf+Y+wP9sWcO/8z8k2RLH7PHf6Nd7qnE/gTrrGs6a6lsaePfo\nR7x37GMa25owhYSzbOp1zB2fSYDWt6dUWa1hWAnjwvHth2XbXG0crzrJgfIjfF1+hNzKfPJrC9Fp\ng0g1J5EckUBSRALJEQkkRsQTHHD+k7/8Sb//1/oyDWhycjIPPPAA1113HUVFRSxbtozNmzf3ezsO\nR0N/m9ers4/pGDFz/fiFbDzxAb/84s/8f9PuGvZvVnfOn0BJRR2f7C0mQh/ITZnj+7W+vxyn8jU1\n1jXUNbW52/jTl+vRKBoWjVvYj/dSuG/aPazd+xt+u/svaFt0TDJP6NOaatxPoM66hqumprYmPin+\ngi2Fn9HY1ogx0MC30m7kyrhvEKQNxFHV2PtG+uF8dUUSzZzoaOZEz6axrQlnSz2RIaauo08eqHO0\nUIf/nFXf0xerXsdirVYrdvuZm11UVFRgsVi6XVZeXo7VaiU6OppFixahKAqJiYlERUVRXl6OXq+n\nqampy2v9wYKkq0kNT+Yr29fsLNs77O8fGKDlgW9lEBUezDvb8tlxqGzY2yDUYVvJDmyNlVwV/w2i\nQ/v3+YoJjeb7FyxDQeH3B/9MqVN+DkX/tLha2FL4Kauyn+dfJzahQeHm1EX8ZOaTzB131bCf8NtZ\nSEAwFn1kn46d+7NeW5+ZmcmmTZsAyMnJwWq1YjC0DyEnJCTgdDopLi6mra2NrVu3kpmZycaNG3n1\n1VcBsNlsVFZWEh0dzcyZM73b2rx5M1dddVX3bzrMNIqG70xdSrBWx5vH/om9sar3lXwsPDSIh7+d\nQYhOyx/+fYTjxTXD3gYxujW0NvB+/hZCAoJZlDyw49JpplTumrKYxrYmfrP/D9Q0q6unKYZGq6uV\nrUWfsyr7ed4+/m9cHhc3jF/IT2Y+yYKkq9Fp5SRbX+nTJV9r165lz549KIrCqlWrOHToEEajkQUL\nFrB7927Wrl0LwMKFC7n33ntxOp08/vjj1NbW0traygMPPMDs2bOpqKjgiSeeoLm5mbi4OJ577jkC\nA8//zWsoL/nqzs5Te/nz4SxSwpN5dPp9I/KN7GB+Jf/3jwOEhgTw2G0XkRjd+/EnNQ7jgTrrGsqa\n3sr9Fx8XbePm1EUsSLp6UNv64ORH/OvEJhKN8Tx88X09Hu9T434Cddbl65qcrfXsqzjAByc/prq5\nBp02iDkJVzIvcRb6QL3P3qc3attXPQ2P9ym0R8pwh7bH4+HVnL+xr+IAN6Zcy7XJc33+/n3xyb4S\n/rzpKAFaDXcsSGP2hXE9HmdX2w9sBzXWNVQ12Roq+enOtUTownjqiscHPTuTx+Ph9SPr2X5qN9Mi\np/CfFyw771m+atxPoM66BltTTXMtx6tPcLw6n+PV+ZTWtx9CCdQEMjthJgsSr8YQNPA5JwZKbftq\nUNdpjyWKonD7pFs5UX2Sf+dvZoo5jaSwccPejqsvjifCqOPVdw/x5w+OcrSwmmXXTCJEJ7tLdO+f\nJ97H5XHxzdTrfDKdoqIoLJ10K47mGg5WHmZ97kaWTLzZ7y5/EUOrstFxOqTbg7qi8cw5TIGaQCaZ\nJpAWkcrMuMsJ16nvTHt/JClwltBAPcum3sYvv/o9fzr0Bk9e9jBBI3A85qIJUTz9H5fz0saD7DxU\nzslTtfzXzdP6NFwuxpYTNSfZV3GA5LBEplsv9Nl2tRot9067ixf3/obPSrKJDDEzP3G2z7Yv/IvH\n48HWaCf3dEDnOk7gaK72Lg/WBpMeOZkJEeOZEJFCojF+QDPoicGR//FuTDanMWfclWwt+py3j/+b\n2ybdMiLtiAwP5ok7prPhsxN8sLOQZ/68t0/D5WLsaJ9I5V0AvpV2g89/LkICgvnBhd/lhT2/4u3j\n/8YcbGK6NaP3Ff2Ux+ORz85pbo+bsvoKjlef8AZ1bcuZIebQAD0XRqUzwZTChIjxJBjiRv2Z12og\noX0e30y5jiNVuXxWkk165GSmRU0ZkXYEaDUsmTOBieMiZLhcnOPLiv3k1xZyseWCIbtjnSk4gv+6\n8Lv8/Mvf8KdDbxChCxvWu+MNltvjZm/5ft4/uQVHcw1JxgSSwxJJDhtHcngiEbrwkW7isHC5XZyo\nKmB34cH2Y9I1+dS3npkLIyzIyCXWC7096ZhQq4S0H5IT0XpQXFfKC3t+SUhACP9zxQ8xBg3vbGln\nq6xp4qWNB8krqSXaFOIdLlfbSRgd1FiXL2tqdbXy051rqW6u5akrHseij/TJds8np/IoLx14DX1A\nCI9dcj9WfRTgv/vJ4/Gw33aQd/M3c6q+HK2iJSokkooGGx7O/NqL0IW3B/jpIE8MG4dOG+S3dfXE\n4/FQ3VyDrdFOeYOdigYbFQ12Khpt2BurukzdaQ42MSFiPGkR7T1pS0jUqB2FGI37qidy9ngn/d25\nWwo/5e3j/+aCqKl8/4LvDMsPtcvtorq5FnNwxDnv1+Zye4fLO84u//b8SdjtziFv13BT2wcRfFvT\nhwWf8E7ee8wddxXfSrvRJ9vszRclO3n96FtYQiJ5/JIHMASF+t1+8ng85FQe4d38zRTVlaCgcEXs\nJSxKnk9kiJnGtiYKa4vJry3k5Ok/dS1nPj8KCnGGGCZbU4kJiiE5LNHvep31rQ1nArnBRnlj+9+2\nBjst7tZzXh8aqMcaYmF8VAIJugQmRKQQGWIagZYPDX/7GRwsOXt8EOaOu4oc+xG+th9ie+kuMuOv\nGLL3amxrZHvpbrYWfY6juZqp5kncPvlWzMFnPlzdDZefLHdy29WpMlw+hjhb6tlU8DGhAXquS543\nbO+bGX8F9qYqNhds5Xdf/4mHLvresL13XxxzHOdfJzZxoqYABYVLoy9iUfL8LrPDhQQEM8k8wTtN\na8ctGU96Q7yIorpiSk6c8q4TrNWRGDauU488ccjPlm5xtWBrrKS8Uzh39Jo7D2t3CNIEYtVbsOqj\nsIZEef9t0UdhCGy/DEtt4TYWSU+7DxxN1Ty760Vcbhc/uvwRrHqLT9tkb6zik+LPyS7dTZOrmUBN\nILGhVgrrSgjW6rh5wvVkxl1+zjf98w2Xq4Uaf8H4qqZ/HPsnnxZ/wbfTbmLOuCt90LK+c3vc/DHn\n7+yt2M/F1gyeuPr7VNrrh7UNZztRU8C/TmzimOM4ABdGpXN9ykLiDbED2p7L7aIhsJZ9BUe8QV7e\nUNHlNSZdBMnhiSQYYlFQaPO4cLtdtHlcuNwuXB4Xbaf/dp1+7uxlbk/3r29zt+FsPff/VKNoiAox\nYw05Hc56C9Gn/+7LHanU+JkC9dUlw+OdDHTn7in/itdyXicpbByPTf+BT+6neqKmgI8LP+Mr20E8\neAgPCmN2wkyujP8G+oAQdpTt5a3cf9HY1sjEiFTunPJtokK6Hrdsc7n5YHcxGz453ufJWEYLtX0Q\nwTc1lTfYeGbn/xIZbOLHVzw2IpfdtLrb+OW+35NXk8/1E+cxL3bOiExVWVhXzLsnNpNTeQSAqeZJ\n3JCy0CfzK5y9rxpaGymoK+JkTZG3V95dsPZHgKJFq9Gi7fR3+3MBhOvCsOqjiO7Ua44MNg/qd48a\nP1OgvroktDsZzM79Y87f2V2+j0XJ87k+ZeGAtuFyu9hvz+Hjws/Iry0EIMEQx7zEWUy3ZpzzC7i6\nuYY3jr7N1/ZDBGkCuSn1OmYnzOzS67ZYjHyYnc+r7x6ivqmNK6ZGq+Lscl9+EOtPz8vd6GoiNTyZ\n1PBkrHrLsH+58UVNvzvwJw7Yc/jetLu5yHqBj1rWf/WtDfzv3l9T3mBDo2hIMo4jzZTChIgUUsOT\nCA4IHrL3LnWW8e/8D/nK9jUAaREp3JByDRMi+neHvJ70tq88Hg+VTQ7K6stRFE2XAA7oFMTdPq9o\n0SiaUfnz54/UVpeEdieD2bkNrY2s3vVzalpqeXT6f5ESntTndRvbmsgu3cXW4i+oanIAcEHUFOaO\nm0VaREqPH16Px8Pe8q/4R+4/qW9tICU8mbumLCb69DB9R01qGy731QfxUOVR/nr4TWpaars8bwgM\nJSU8mdSI9hAfNwyTRQy2plxHHv+373ekho/n0en3jfiISnVzDTsrd7G/9AhFdSXes5M1ioZxxnjS\nIlJIi0ghNSKZkICQQb9fRYOd9/I/ZE/5V3jwkBQ2jptSrmWSaYLP/y/UFgSgzppAfXVJaHcy2J17\nzJHHun0vExli5keXPdLrjdMrGx18Uvw520t3eY9XfyP2UuYkZPb71om1LXX849g/2VdxgEBNANeP\nX8jccVcREx3hram7s8tH63D5YPdVi6uFt4+/x2cl29EoGq4fv4BpkVM4UXOSvJqT5FWf7DLjU6Am\ngKSwcaSGjyclPImU8GT0gYMPms4GU5Pb42bNnl9SVFfC8ksfHJEpdrvTUVNTWxMnagrIrT5BruME\nBXVF3hBXUEgwxnlDfELE+H7dUKKqycH7+R+xo2wPbo+beEMsN6Zcw7TIKUP2s622IAB11gTqq0tC\nuxNf7Nx3jr/Hh4WfMDP2cu6c8u1uX5NfU8jHRZ+xr+JrPHgICzIyOyGTK+Ov8J7JOVD7Kr4m6+jb\n1LU6STKO46HMewhu6bqTv8q18+q/R/dw+WD21cnaQv506A0qGuzEhEbznam3kWhMOOd1jqZqb4Dn\n1eRT6izzXsOroBAbGk3K6Z54angy5mDToEJiMDV13IXu0uiL+I/0OwbcBl87X03NrhbyO4d4bSFt\nHhdw5rKqMyGe0u2NJmqaa9lU8DFflOykzeMiWm/lhpSFXGSZNuSXYKktCECdNYH66pLQ7sQXO7fV\n3cbaPb+i2FnKf16wjAst04Azx6u3Fm3jRE0BAPGGWOaNm8X06AsJ9OHQq7O1nvXHNrK7fB9ajZbr\nkuazMOnqLiepVNY08dI/D5JXOjqHyweyr1xuFx+c/IgPCj7G7XEzd9xV3JRybZ9votHY1kh+TSF5\nNSc5UX2S/NpCWjtd9xqhCyc1PNk7rB5viO1XeAz056/F1cpPdqzB2VrPyiv+26+use1rTS2uVk7W\nFpDraJ82M7+2kDZ3m3d5XGgMEyJSSDOlEB8awxendvFZ8XZa3W1EBZtZNH4Bl8VcPGzXS6stCECd\nNYH66pLQ7sRXO7fUWcbze9ah0wbx2CX3k1N5hE+KPqfy9PHqaZGTmTtuFhNNqUM6NP21/RBZuW/j\naKwhwRDHXVOWMM4Y511+9nD5rbNSmHdJAoEB/jNRxPn0d1+V1Vfwp0NvUFhXjEkXwd1TlnivxR0o\nl9tFkbOEE9VnhtTrWs9MxKHTBjEhIoXLY6ZzYVR6r18OBvrz98HJj/nXiQ9YmDSHb6Ze1+/1h9JA\na2p1t1FQW3Q6xPM4UVPQ5QsStH9Jui55HjNiL/PJFRv9obYgAHXWBOqrS0K7E1/u3K1Fn7M+d6P3\ncaAmgCtiLmHOuKuI6efx6sHQh2t5eccbZJ/ajUbRsDBpDtcmz+vSs//quN17dnlUeDC3zkrh8qnR\naPz4WHdf95Xb4+az4mzeyfs3re42Lo+ZzuK0b/r8eDR03Amp0tsTz6vJp7zBBoA+IITLYi5mRuxl\njDPGd7v+QH7+alvqeDr7eQI1gTw94wlChvCs7IHw1Weqzd1GYV0xuY4TFNaVMCFiPFfGXeGTW40O\nhNqCANRZE6ivLgntTny5c90eN68e/Csna4u4Mu4bXBX/jRG9AfzhymP87ch6HM3VxIZGc9eUxSSH\nJXpf52xs5d3tJ/n4y2LaXB6Soo0snpPK1GTzsLe5L/qyrxxN1fz18JscceQSGqjn9knf4uJhvgyq\nvL6C7FN72Fm213uXpARDHDNiL+PSmIu6nMMwkJ+/vx95i89Ld3LbxJuZlTDTp233BbX9wuygxrrU\nWBOory4J7U7UtnOha01NbU28k/c+20qyUVCYlziL68cvJKhTb8VW3cjbn51gx6FyAKalmFl89QTG\nWUf2hihn621f7SnbxxvH3qGxrZH0yMncOfnbhOvChrGFXbncLg5VHSW7dDdfVx7G7XEToGjJsKQz\nI/YyJpvTiLaG9+vnr9RZxupdP8eqt/A/lz867EPEfaHGzxSosy411gTqq0tCuxO17VzovqZjjjz+\ndvhN7E1VWPVR3DV5CakRyV1ec7Kslje35nG4wIECzJwWw81XpRAZ7h/Dr+fbV/WtDWQdfZu9FfsJ\n0gRya9qNXBl3hV9d1lbbUseusi/JLt1N2enpL026COamziQj/IJzZrY7n1/vf5VDlUe5L+MeLoia\nOpRNHjA1fqZAnXWpsSZQX10S2p2obedCz5fc/OvEB3xS9AUAsxNm8o3YSwEFTl/W5PF4OF5Sw5Y9\nxZQ7GtBqNXxjipXMjBiCg7qe7d75doadf2qCA3RYQ6J83gvsrq7OE6WMD0ti2dTbvLeI9Ecej4eT\ntYVkn9rN3vL9NLmaAZgYkcqMuMu4yDKNoPNM/3m46hi/+uoVJpom8NBF3/OrLyWdqfEzBeqsS401\ngfrqktDuRG07F3qvKa/6JH898g8qGuxD1oZATSAJhjgSw+JJNCaQaEwgWm/x2TzJ506UspAFibP9\ncrj4fJpdLeQ15rL52DZyq08AEKwN5tLoC5kZdzmJxgRvMLs9bn62+xeUOst44rKHu1wR4G/U+JkC\nddalxppAfXXJrTnHuNSIZH502aN8VrKd6qaaMwuUzv88HRZuD4XldZworaPV5SZEF0BaQjhxkaGg\nnHld53XqWp0U1ZVQUFdEfm2Bd3nnIB9nTCBpgEHe14lS/J1OG8Ts8d9gqiGdigY7O07tYcepPXxe\nupPPS3cSFxrDjNhLuSxmOl/bD1PiPMU3Yi7168AWQgwv6WmrwFDUdPaZ5onRBpbMmdDjmeYtrlZK\nnKcorCumsK6YoroSTtWXe6eyhHODPNEYT4ze2m2QmyL1/HX3O10mSrkx5douJ9WNNmfvK7fHzeGq\nY2SX7uaA/RAuj8t7YwmPx8OqGcuJ0IWPYIt7p8bPFKizLjXWBOqrS4bHO1HbzoWhrcle3ciGbSfY\nkXP6TPPxZr59dWqfZ1brCPKiumIK60oorCvuU5ArKLyR+xZ5jgKfTZTiD3raV86WenaX72N76S5K\n68u4Yfw1XDd+3jC3sP/U+JkCddalxppAfXVJaHeitp0Lw1NTQVkd/9h63Hum+YxpMdwywDPN+xLk\nHYZyopSR0Jd95fF4qG6uIUIX7rcnn3Wmxs8UqLMuNdYE6qtLjmmLQUuKMfL40ovIya/iH1vz2H6w\njF2HK5h/aQLXz0giNLjvQ9ZB2kDGhycyPvzMxC9nB7mjqZpFU64mNThtKMrxa4qiYAqOGOlmCCH8\nkIS26DNFUZiWEsnUZDPZOWW8va19TvNNOwsJCw3CHKbDbAzGdPpvc5gOc1gwZqOOCIMOjeb8vcbu\nglxt356FEGKwJLRFv2k0CpkXxHL5FCsf7S3hQJ6dqtpmiiqc5J/qPmQ1ikKEMehMmHcT7kZ9oF/P\nhS6EECOtT6G9evVq9u/fj6IorFixgoyMDO+y7du38+KLL6LVapk1axb3338/AGvWrGHv3r20tbXx\n/e9/n4ULF/Lkk0+Sk5NDRET70N+9997L1Vdf7fuqxLAIDNBy7RWJXHtFe+/Y4/FQ19BKZW0TVbXN\nVNU14Tj9d8fjE6W1HC/p/jSKAK2Cydge5JHhwUxMMmM2BJJoNRIW2v0EJEIIMZb0Gtq7du2ioKCA\nrKws8vLyWLFiBVlZWd7lzzzzDK+++irR0dHcddddXHPNNdjtdnJzc8nKysLhcHDLLbewcOFCAH74\nwx8yZ86coatIjBhFUQgLDSIsNIjxsd2/xuV2U+Nsoaqumapuwr2ytpmjRdVQBNsPlnnXCzcEkWg1\nkhhtYJy1/U+0Sd/jkLsQQqhNr6GdnZ3N/PnzAUhNTaWmpgan04nBYKCoqIjw8HBiY9t/Q8+ePZvs\n7GzuuOMOb288LCyMxsZGXC7XEJYhRgutRtN+nDssGOK7v/64tc1NVW0TNc0uDubaKK5wUlhRx9cn\nKvn6RKX3dUGBGsZZDIyLNpJoNTAu2kCCxYAucPTMkiaEEP3Ra2jb7XbS09O9j81mMzabDYPBgM1m\nw2w2d1lWVFSEVqtFr9cDsH79embNmoVW2/6L9K9//SuvvfYakZGRPPXUU13WP5vJpCcgwPe/gHs6\nnX60UltNcbHtgZ6ZcWY2sNr6FvJLa07/qeVESQ0ny+rIK631vkajQGyUgZT4cMbHhZESH05KXDim\nMP+4CQqob1+BOmsCddalxppAvXWdrd8novXnsu4tW7awfv16/vCHPwDwzW9+k4iICKZMmcLLL7/M\nr371K1auXHne9R2Ohv42r1dqPCNZjTVB93XFRQQTFxFM5tRooL1XXmqvp7CijqIKJ0XlTgornJTY\nnGz7qsS7XlhoEOOsBlLjwpiabCYlLowArWZY6wF17is11gTqrEuNNYH66hrUddpWqxW7/cyNJioq\nKrBYLN0uKy8vx2q1ArBt2zZeeuklXnnlFYzG9gbMmDHD+9q5c+fy9NNP968SIc4SGKAhKcZIUsyZ\nH3KPx0NlTROFFU4Ky0+HeYWTnPwqcvKr2PjFSXSBWiYlRjA1ycSUZDMJltBRMZGJEGJs6zW0MzMz\n+eUvf8nSpUvJycnBarViMBgASEhIwOl0UlxcTExMDFu3bmXt2rXU1dWxZs0a/vjHP3rPFAd48MEH\nWb58OePGjWPnzp2kpY29iTPE0FMUhaiIEKIiQpg+0eJ9vr6plWOF1RwqcHDoZBUH8io5kNd+jDxM\nH8iUZPPpEDcRFa6OGdiEEOrSa2hPnz6d9PR0li5diqIorFq1ig0bNmA0GlmwYAFPP/00jz32GACL\nFi1i/Pjx3rPGH3nkEe92nn/+ee68804eeeQRQkJC0Ov1PPfcc0NXmRBnCQ0O5OKJFi4+HeSOumYO\nnazi0EkHhwuq2HmonIOQt7AAABAmSURBVJ2H2udYt5pCmHo6xCcnmTCEjN6blAgh1EPmHlcBNdYE\nw1uXx+PhVGWDN8SPFDpoamm/4kEBEmOMTE02MTXZTFp8OEEDPENdjftKjTWBOutSY02gvrpk7nEh\neqEoCnFRocRFhTL/0nG43G7yT9Vx+HSIHy+poaCsjvd3FBKg1ZCWEO4N8aRoo1wvLoQYFhLaQnRD\nq9EwIT6cCfHh3Jg5nuYWF8eKqzl8sv14+OECB4cLHLz16QmCg7QkWM5M+pJgNZBgCSU4SD5eQgjf\nkt8qQvSBLkjLBSmRXJASCUBtQwtHChzeXnj79Kw1XdaxRoR4Q7zj78hIw0g0XwihEhLaQgxAmD6I\ny6dEc/mUjuvFXZTaGyisqKO4op5iW/tlZnuP2dh7zOZdL0QXQHxUaJcwj48KJUQnH0UhRO/kN4UQ\nPhAYoO32evFqZwtFFU5viJ+qajhvr9zbI7e0T8lqCQ+Wa8eFEF1IaAsxRBSl/a5lJqOOjNT2YXWL\nxUjpqWpK7Q1dwryowsmXx2x82aVXrj19kxQjSTEGkqKNxETq0WqGfyY3IYR/kNAWYpj11CvvHOKF\n5XUcK6puv+uZd10N46yG9iCPbv87wRJK4BDM0d+Zy+2msqaJiupGKhyd/lQ3Ut/Uil4XgFEfhFEf\nSNjpv4369ju+hekDMejb/w4NkXumCzEYEtpC+IHOvfKOk90AmlraKK6op6C8joLyOgrL6igoq+NE\np5ukaDUKsZGhJMV0hLmRcVZDv4+Tt7a5qKhuwuZopMLRcCagqxuprGnC5T53SocQXQDmMB01zhbK\nKhvobdIHRQFjSCDG0CCMIYGEhQadN+yjwoNHZH54IfyZhLYQfiw4KIAJCeFMSDhzG9OOm6R4g7y8\njqLy9qH2L75uvwe5AljNepKi24fVE2Paw1yrUbCdDuNyR4P33xXVjThqm7sN3TB9IMmxRqwReqJN\nIVhMIVhNIVgjQjCEBGK1hmGz1eF2e3A2tlLX0EJtQ/vfdQ2t1Na3nPn36WXVdc2U2Op7rF2rab92\nvuNSuo4/Rn2QD/+HhRhdJLSFGGW6u0mK2+2hrKrBG+IFZXUUljvZdbiCXYcret2mOUzHpMQIrKYQ\nLBEhRJv03n/3tceu0Sjtw+GhQcT34fVtLjfOxo5Q7xr2Nc4WSuz1lJw+XNBZhCGIcVZjlyCPNofI\nsX4xJkhoC6ECGs2ZGd1mpMcA7cfJ7TVN7QFeUUdBmROPx+PtJVtNeiymECzhwQOelnUwArQaIgw6\nIgy6877G7fZQ7mjwHufv+PP1iUq+PlHpfV1ggMZ7KV3nP/pgmTNeqIuEthAqpSgKloj23vKlk60j\n3ZwB0Zw+Xh8bGeq9Jh7A2djaKcTrvGfinyzrOv90ZFhwlxCPt4RiiQiRY+Vi1JLQFkKMOoaQQKYk\nmZiSZPI+1+ZyU1Z1bq/8/2/v3mOjqPY4gH9n9sm2pS/biiFF0iggV7QqyiO0gIJC4is3MdZs0KRG\nBUoNQdtixDZRrNDViJWorW8RQ6zG1EcCMfqHwbIgIlqIFxtytQr2XWjpY7uz5/4x3eluu9vdVsru\nzP1+kmY758y055czk9/O65yfmtrxU1O7tp4kqck8M3XkNoD640Bmin3Kn8Qn+ieYtInIEMwmGTMz\n1MFpFs8fKT93waOdjZ/t6NMewjv5X3UY2kAS1Pv7makOZM+YjuRpFi2pZ6RMgy0GtxGIAjFpE5Gh\nJSdYkTw7Hf+anR5UPuhRhl9r60OL/zW3rn60dPVrE8KMlppkQ1bQmbmazBPsZjjsZtitZs74RlOK\nSZuI/i/ZrCbtXvdog0MKFEnGr6fbtTPz1uHE/p8/uvHrH90h/qLKbjVhms0Mh82sftrVT/XHNFJu\nGykP/N1uM3EAGgqLSZuIaBSbxYSMjCQ4zGOT55BXQVv3gJbI288NoG/Ai/7BkZ++QS+6ewdxpuMC\nRKQRZ0aRANhtZpiiPGMXE/gHsixDltQH/EyyBJNJhlmWApYlmGRZ/T1gneBldR3/NhazjMRp6uA4\n6mA5Fu2Tr+FdfEzaREQTYDGbtNfrIhFCYHBIQf+ggr5BL/oH1IQ+OsFrZQP+MmVCyRhRnpjLsgyP\nxwvFJ6D4BDxeLxTFB58QUBShlV8sicOj3k0fNaxtUoIVyQ4rkoaXpydYYbOYOEFOFJi0iYimiCRJ\nsFvVe92pSeHfR79UMjKS0NbWM+46QqiJ2+cbSeKK4hv5fdTykHdkkJzzfR70XBjCuT4PeoaXz/UO\n4kz7+KPfAYDVLAcldrNJhk+o7fAJwOfzDX8Ktdxf5wNkkwTPkDK87K+Dtp4YbqsQCBj1TwzHG1Am\n/B8i4HcEXC0Z2UZrt0VG8b8XYE72yJsMU4lJm4iINJIkwWySgIv4oHzg6HdaYh8e3vb88BC3/uXm\n1h54lfHP9iWol/hlWYIsqZfstTLJX64OhWsZvtwvSxLUE3n/Z8AFCgmQEFwoAZACVgi3jdUiY3rC\npRtal0mbiIimVDSj3/kJIdA/qEDx+UYlYQmyjOHkG3wZPZorCEbBpE1ERHFDkiQ47ExN4fDRPiIi\nIp1g0iYiItIJJm0iIiKdYNImIiLSCSZtIiIinWDSJiIi0omonqt//vnncfz4cUiShKeeegoLFizQ\n6r7//nu89NJLMJlMyMvLw8aNG8Nuc/bsWZSUlEBRFGRkZKCqqgpW66V7KZ2IiEjPIp5pHz58GL//\n/jv27duH7du3Y/v27UH1zz33HKqrq/HRRx/h4MGDaGpqCrvNK6+8ggceeAB79+7FrFmzUFdXNzVR\nERERGVDEpN3Q0IDbbrsNAJCTk4Nz586ht7cXANDc3Izk5GTMmDEDsiwjPz8fDQ0NYbdxu9249dZb\nAQArVqxAQ0PDVMVFRERkOBEvj7e3t2P+/PnaclpaGtra2pCYmIi2tjakpaUF1TU3N6OrqyvkNv39\n/drl8PT0dLS1tY37v1NTHTCbL+IAuMMyMpIu+t+MNSPGBBgzLsakH0aMy4gxAcaNa7QJjxU3oeni\nxtkmmr/T1dU34f8ViRHHqDViTIAx42JM+mHEuIwYE2C8uMb7AhIxaWdmZqK9vV1bbm1tRUZGRsi6\nlpYWZGZmwmKxhNzG4XBgYGAAdrtdW3eyDf8njPiNzIgxAcaMizHphxHjMmJMgHHjGi3iPe2lS5di\n//79AIATJ04gMzMTiYmJAICZM2eit7cXf/75J7xeL7799lssXbo07DZLlizRyg8cOIBly5ZNVVxE\nRESGI4korlO7XC788MMPkCQJ5eXlOHnyJJKSkrBq1SocOXIELpcLALB69WoUFhaG3Gbu3LlobW1F\naWkpBgcHccUVV6CyshIWi2VqIyQiIjKIqJI2ERERxR5HRCMiItIJJm0iIiKdYNImIiLSCSZtIiIi\nnZjw4Cp6MZlJTvRg586dOHr0KLxeLx599FGsXr1aq1u5ciUuv/xymEzqKHIulwtZWVmxampU3G43\nHn/8cVx11VUAgKuvvhrbtm3T6vXYVx9//DHq6+u15cbGRhw7dkxbnj9/Pm644QZt+d1339X6LB6d\nOnUKGzZswEMPPQSn0xnVxD/jHX/xIlRcW7duhdfrhdlsRlVVlTYmBRB5X40Ho2MqKyvDiRMnkJKS\nAgAoLCzE8uXLg7bRY18VFxejq6sLANDd3Y3rr78ezz77rLb+p59+il27diE7OxsAsGTJEqxfvz4m\nbb/ohAG53W7xyCOPCCGEaGpqEvfdd19Q/Zo1a8SZM2eEoiiioKBA/Pbbb7Fo5oQ1NDSIhx9+WAgh\nRGdnp8jPzw+qX7Fihejt7Y1Byybv0KFDYtOmTWHr9dpXfm63W1RUVASV3XzzzTFqzcRduHBBOJ1O\n8fTTT4sPPvhACCFEWVmZ+Oqrr4QQQrz44oviww8/DNom0vEXD0LFVVJSIr788kshhBB79uwRO3bs\nCNom0r4aa6FiKi0tFd98803YbfTaV4HKysrE8ePHg8o++eQT8cILL1yqJl5Shrw8PplJTvRg4cKF\n2LVrFwBg+vTp6O/vh6IoMW7V1NFzX/nt3r0bGzZsiHUzJs1qtaK2tjZo9MJIE/+Md/zFi1BxlZeX\n4/bbbwcApKamoru7O1bNm5RQMUWi177yO336NHp6euLy6sBUMWTSbm9vR2pqqrbsn7AEQMhJTiJN\nXBIvTCYTHA4HAKCurg55eXljLquWl5ejoKAALpdrUuPEx0JTUxMee+wxFBQU4ODBg1q5nvsKAH7+\n+WfMmDEj6BIrAHg8HmzZsgX3338/3nnnnRi1Ljpmsxl2uz2oLNLEP+Mdf/EiVFwOhwMmkwmKomDv\n3r248847x2wXbl+NB6FiAoA9e/Zg3bp12Lx5Mzo7O4Pq9NpXfu+//z6cTmfIusOHD6OwsBAPPvgg\nTp48OZVNvKQMe087kF6SV7S+/vpr1NXV4e233w4qLy4uxrJly5CcnIyNGzdi//79uOOOO2LUyuhc\neeWVKCoqwpo1a9Dc3Ix169bhwIEDY+6R6lFdXR3uvffeMeUlJSW46667IEkSnE4nbrrpJlx77bUx\naOE/F82xpafjT1EUlJSUYNGiRVi8eHFQnR731bvvvhspKSmYN28eampq8Oqrr+KZZ54Ju76e+srj\n8eDo0aOoqKgYU3fdddchLS0Ny5cvx7Fjx1BaWorPP//80jdyChjyTHsyk5zoxXfffYfXX38dtbW1\nSEoKHiD/nnvuQXp6OsxmM/Ly8nDq1KkYtTJ6WVlZWLt2LSRJQnZ2Ni677DK0tLQA0H9fud1u5Obm\njikvKChAQkICHA4HFi1apIt+CuSf+AcI3SfjHX/xbuvWrZg1axaKiorG1I23r8arxYsXY968eQDU\nB1VH72t67qsjR46EvSyek5OjPXCXm5uLzs5Ow9xKNGTSnswkJ3rQ09ODnTt34o033tCeBg2sKyws\nhMfjAaDu0P6nXONZfX093nrrLQDq5fCOjg7tiXc991VLSwsSEhLGnIWdPn0aW7ZsgRACXq8XP/74\noy76KVCkiX/GO/7iWX19PSwWC4qLi8PWh9tX49WmTZvQ3NwMQP0SOXpf02tfAcAvv/yCuXPnhqyr\nra3FF198AUB98jwtLS2u39CYCMOOPT6ZSU7i3b59+1BdXY3Zs2drZbfccgvmzJmDVatW4b333sNn\nn30Gm82Ga665Btu2bYMkSTFscWS9vb144okncP78eQwNDaGoqAgdHR2676vGxka8/PLLePPNNwEA\nNTU1WLhwIXJzc1FVVYVDhw5BlmWsXLkyrl9FaWxsxI4dO/DXX3/BbDYjKysLLpcLZWVlYyb+2bx5\nMyorK2G320NOGBRPQsXV0dEBm82mJa2cnBxUVFRocXm93jH7an5+fowjGREqJqfTiZqaGkybNg0O\nhwOVlZVIT0/XfV9VV1ejuroaN954I9auXautu379erz22mv4+++/8eSTT2pfjuP1VbbJMGzSJiIi\nMhpDXh4nIiIyIiZtIiIinWDSJiIi0gkmbSIiIp1g0iYiItIJJm0iIiKdYNImIiLSif8BI3GFA9kn\nUSUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff6c4dc0e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HcWQMhSa0NLJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "57000821-2c8c-42cf-a0d6-06ee4180e9b0"
      },
      "cell_type": "code",
      "source": [
        "loss, acc = model2.evaluate(test_images.reshape(10000,28,28,1),test_labeks)\n",
        "print(acc)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 2s 159us/step\n",
            "0.9926\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Aa1jimx22LMl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}